Bayesian networks for satellite payload testing

Krzysztof Wojtek Przytula; Frank Hagen; Kar Yung

doi:10.1117/12.367698

1 November 1999 Bayesian networks for satellite payload testing

Krzysztof Wojtek Przytula, Frank Hagen, Kar Yung

Proceedings Volume 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II; (1999) https://doi.org/10.1117/12.367698
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

Abstract

Satellite payloads are fast increasing in complexity, resulting in commensurate growth in cost of manufacturing and operation. A need exists for a software tool, which would assist engineers in production and operation of satellite systems. We have designed and implemented a software tool, which performs part of this task. The tool aids a test engineer in debugging satellite payloads during system testing. At this stage of satellite integration and testing both the tested payload and the testing equipment represent complicated systems consisting of a very large number of components and devices. When an error is detected during execution of a test procedure, the tool presents to the engineer a ranked list of potential sources of the error and a list of recommended further tests. The engineer decides this on this basis if to perform some of the recommended additional test or replace the suspect component. The tool has been installed in payload testing facility. The tool is based on Bayesian networks, a graphical method of representing uncertainty in terms of probabilistic influences. The Bayesian network was configured using detailed flow diagrams of testing procedures and block diagrams of the payload and testing hardware. The conditional and prior probability values were initially obtained from experts and refined in later stages of design. The Bayesian network provided a very informative model of the payload and testing equipment and inspired many new ideas regarding the future test procedures and testing equipment configurations. The tool is the first step in developing a family of tools for various phases of satellite integration and operation.

Citation Download Citation

Krzysztof Wojtek Przytula, Frank Hagen, and Kar Yung "Bayesian networks for satellite payload testing", Proc. SPIE 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, (1 November 1999); https://doi.org/10.1117/12.367698

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